Creating a dynamic aggregate group profile of users in an online collaboration session for providing tailored content delivery

ABSTRACT

There are provided a system, a method and a computer program product for suggesting content to a group. The system identifies a group of users in a social network. The system receives inputs associated with the social network group. The system aggregates the received inputs. The system analyzes the aggregated inputs. The system searches, based on the aggregation and the analysis, one or more tailored content to be delivered to the group in Internet, one or more database and one or more data warehouse. The system delivers the tailored content to the group in the social network.

BACKGROUND

This disclosure relates generally to delivering content to a group ofusers, and particularly to delivering content to a group of users basedon one or more profile of the group.

BACKGROUND OF THE INVENTION

In a social network, users may engage in group interactions, forexample, an online forum discussion or a real-time online chat, and mayshare content or engage in activities together. Currently, an individualuser may manually choose particular content (e.g., ringtone, e-book, MP3file, etc.), e.g., via an online application store. Then, acorresponding sever device that hosts the online application storedelivers the chosen content to the individual user, e.g., via one ormore communication network.

SUMMARY

There are provided a system, a method and a computer program product forsuggesting content to a group. In one embodiment, the system identifiesa group of users in a social network. The system receives inputsassociated with the social network group. The system aggregates thereceived inputs. The system analyzes the aggregated inputs. The systemsearches, based on the aggregation and the analysis, one or moretailored content to be delivered to the group in Internet, one or moredatabase and one or more data warehouse. The system delivers thetailored content to the group in the social network.

In order to analyze the aggregated inputs, in one embodiment, the systemclassifies the aggregated inputs. The system identifies, based on theclassification, a common interest, a common preference and a common needof the one or more users in the social network group. The system furtheridentifies, based on the classification, a common interest, a commonpreference and a common need of a majority of the one or more users inthe social network group.

In one embodiment, the method for suggesting content to a groupcomprises identifying a group of users in a social network. Inputsassociated with the social network are received. The received inputs areaggregated. The aggregated inputs are analyzed. One or more tailoredcontent to be delivered to the group is searched, based on theaggregation and the analysis, in Internet, one or more database and oneor more data warehouse. The tailored content is delivered to the groupin the social network.

In one embodiment, a computer program product for suggesting content toa group comprises a computer readable storage medium. The computerreadable storage medium is readable by a processing circuit and storesinstructions run by the processing circuit. The instructions identify agroup of users in a social network. The instructions receive inputsassociated with the social network group. The instructions aggregate thereceived inputs. The instructions analyze the aggregated inputs. Theinstructions search, based on the aggregation and the analysis, one ormore tailored content to be delivered to the group in Internet, one ormore database and one or more data warehouse. The instructions deliverthe tailored content to the group in the social network.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings, in which:

FIG. 1 illustrates a flowchart that describes a method for suggestingcontent to a social network group in one embodiment;

FIG. 2 illustrates a flowchart that describes a method for aggregatingof inputs in one embodiment;

FIG. 3 illustrates exemplary hardware configurations for performingmethods shown in FIGS. 1-2 in one embodiment; and

FIG. 4 illustrates a flowchart that describes a method for analyzing theaggregated inputs in one embodiment.

DETAILED DESCRIPTION

When an online collaborative session (e.g. a social network groupinteraction, a group chat, etc.) among a number of contacts (i.e.,users) in a social network takes place, a tailored content can bedelivered to the contacts, e.g., via the social network. Based oncommonalities (e.g., common interests, etc.) in profiles of the usersinvolved in the online collaborative session along with users' commonactivity history, the social network group can be characterized for atargeted content suggestion that is most relevant to that group as awhole. Accordingly, contents delivered to the group may also be furthertailored to be a particular content type to meet an aggregatedpreference of the users based on content consumption history of theusers, e.g., news articles read by one or more of the users during alast month.

There are provided a method, a system and a computer program product forsuggesting tailored content to users in a group based on a group profilewhich may be dynamically updated as one or more users join or leave thegroup. A computing system (e.g., a computing system shown in FIG. 3) maycreate the group profile of the users, e.g., by aggregating (e.g., amethod shown in FIG. 2 described below) individual user profiles andfurther identifying commonalities of the users in the aggregated userprofiles. FIG. 1 illustrates a flowchart for suggesting tailored contentto a social network group in one embodiment. At 100, the computingsystem identifies a group of users in a social network, e.g., by using aweb address (for example, a uniform resource locator that locates a webpage on the Internet or the like) of a social network page whichcorresponds to the social network group. One or more users in the socialnetwork group may provide that web address to the computing system. In afurther embodiment, the computing system identifies a subgroup withinthe social network group, e.g., by using a web address of a socialnetwork page that corresponds to the subgroup. One or more users in thesocial network subgroup may provide the web address of the socialnetwork subgroup to the computing system. In one embodiment, thecomputing system may be a server device hosting the social network. Inanother embodiment, the computing may be a computer associated with thesocial network.

At 110, the computing system receives inputs associated with the socialnetwork group, e.g., from a server device hosting the social network orfrom a database (not shown) associated with the social network. Thereceived inputs include, but are not limited to: (1) social networkpostings (e.g., comments, status postings, etc.) of one or more of theusers in the social network group, which are stored in the server devicehosting the social network; (2) previous or real-time chat sessions ofthe one or more users in the social network group, which have beenstored in the server device hosting the social network; (3) photographsof the one or more users in the social network group, which are storedin the server device hosting the social network; (4) previous orreal-time videos of the one or more users in the social network group,which have been stored in the server device hosting the social network;(5) profile data of the one or more users in the social network group,which are stored in the server device hosting the social network; and(6) previous or real-time interaction data of the one or more users inthe social network group, which have been stored in the server devicehosting the social network.

The profile data of the one or more users include, but is not limitedto: (a) data representing ages of the one or more users; (b) datarepresenting current or previous geographic locations of the one or moreusers; (c) data representing interests of the one or more users; and (d)data representing hobbies or likeness of the one or more users. In afurther embodiment, the computing system may create a single or multiplegroup profile(s) or concept(s), e.g., by aggregating the received inputsand identifying commonalities of the users in the aggregated inputs. Forexample, assume that a social network group is formed to share anactivity, e.g., golfing, etc. Most of users in that social network groupmay indicate “golf” as hobbies, e.g., by selecting an icon (not shown)corresponding to the “golf” when creating a profile of each user in thesocial network. By receiving all the profile data of the users in thesocial network group from the server device hosting the social networkor the database associated with the social network, the computing systemmay determine that most of the users in the social network group areinterested in the “golf.” Then, the computing system may determine thata commonality of the social network group is the “golf.” The computingsystem may include the “golf” in the group profile of the social networkgroup.

In one embodiment, a social network group dynamically changes one ormore users in the group, e.g., as a user joins or leaves the group. Thecomputing system dynamically updates, in real-time, the received inputsin order to reflect the dynamically changed users in the group. Forexample, the computing system performs a dynamic real-time analysis of acurrent online discussion held in the social network, e.g., by running amethod shown in FIG. 3 which is described below. The computing systemmay assign a uniform numerical weight to each user profile joined thecurrent online discussion. The computing system may adjust a numericalweight of an existing profile of a social network group user as thatsocial network group user leaves and/or joins the current onlinediscussion. The computing system stores, e.g., in a data storage device,the uniform numerical weight and the adjusted numerical weight(s)corresponding to the social network group user(s) who leave and/or jointhe current online discussion.

Returning to FIG. 1, at 120, the computing system aggregates the inputsreceived at 110. In one embodiment, the computing system aggregates thereceived inputs as well as metadata of the received inputs. In thisembodiment, the computing system may receive or retrieve the metadata ofthe received inputs, e.g., from the server device or the database of thesocial network. The metadata of the received inputs include, but is notlimited to: (1) a day and time that the server device stored acorresponding received input in a storage device (not shown) or adatabase; (2) a GPS (Global Positioning System) coordinate of a currentor previous location of each user in a corresponding social networkgroup, etc.

In one embodiment, in order to aggregate the received inputs (and themetadata of the received inputs), the computing system runs a methodshown in FIG. 2. At 200, the computing system searches data, which isassociated with the one or more social network group users, in thesocial network, Internet or one or more database which may be associatedwith the social network or other social network(s). In a furtherembodiment, the computing system accesses the database(s) of the socialnetwork or of the other social network(s), e.g., by automaticallyproviding valid credential(s) to the social network and/or the othersocial network(s). The computing system may store the valid credentials,e.g., in a data storage device (not shown). The searched data include,but is not limited to: (1) the data same or similar to the receivedinputs; (2) additional data (e.g., email address(es), etc.) associatedwith the one or more users, etc.

Returning to FIG. 2, at 210, the computing system gathers the searcheddata, e.g., by storing electronic files of the searched data in a datastorage device (not shown). At 220, the computing system summarizes thegathered data, e.g., by running content analysis or text mining softwareon the gathered data. At 230, the computing system presents thesummarized data in a report. In one embodiment, the run content analysisor texting mining software outputs the summarized data as one or morereports.

Returning to FIG. 1, at 130, the computing system analyzes theaggregated inputs. The computing system analyzes the aggregated inputs,associated with a corresponding social network group, as a whole ratherthan analyzing data (e.g., a current geographic location, etc.) of anindividual user. In one embodiment, in order to analyze the aggregatedinputs as a whole, the computing system runs a method shown in FIG. 4.At 400, the computing system classifies the aggregated inputs, e.g., byapplying a classification technique on the aggregated inputs. At 410,the computing system identifies, based on the classification, a commoninterest, a common preference, a common need, and a common geographiclocation of the one or more users. At 420, the computing system furtheridentifies, based on the classification, a common interest, a commonpreference and a common need, and a common geographic location of amajority of the one or more users. For example, if a social networkgroup is formed for enjoying hobbies together, individual profiles ofusers in the group may indicate the hobbies of the individual users. Anindividual user may indicate his/her hobby, e.g., in selecting one in apre-determined drop-down menu (not shown), which lists a plurality ofhobbies, in a web page (not shown) used for creating his/her socialnetwork profile. The computing system may classify those users as one ormore classes according to the hobbies indicated on the profiles. Eachclass may represent a particular hobby shared by members in that class.The computing system may identify the particular hobby, which the grouprepresents, as a common interest of those members.

Returning to FIG. 1, at 140, the computing system searches, based on theaggregation (e.g., a method shown in FIG. 2) and the analysis (e.g., amethod shown in FIG. 4), one or more tailored content to be delivered tothe group in Internet, one or more database and one or more datawarehouse. The computing system may store, e.g., in a data storagedevice, etc., valid credentials to access that one or more database andthat one or more data warehouse. In order to access the database and thedata warehouse, the computing system may automatically provide thestored valid credentials to the database and the data warehouse.

In one embodiment, in order to search the tailored content, thecomputing system runs an Internet search engine with a keyword foundfrom the aggregation (e.g., a method shown in FIG. 2) and/or theanalysis (e.g., a method shown in FIG. 4). In one embodiment, the foundkeyword represents one or more of: the common interest of the one ormore users, the common preference of the one or more users, the commonneed of the one or more users, the common geographic location of the oneor more users, the common interest of the majority of the one or moreusers, the common preference of the majority of the one or more users,the common geographic location of the majority of the one or more users,and the common need of the majority of the one or more users, which areidentified at 410-420 in FIG. 4. After running the search engine withthe found keyword, the computing system identifies one or more contentwhich are included in a search result of the search engine. In thisembodiment, the tailored content includes, but is not limited to: theone or more content included in the search result.

In one embodiment, the one or more content included in the search resultincludes, but is not limited to: one or more e-books, one or more onlinemagazines, one or more online videos, one or more online news articlesthat correspond to one or more of: the common interest of the one ormore users, the common preference of the one or more users, the commonneed of the one or more users, the common geographic location of the oneor more users, the common interest of the majority of the one or moreusers, the common preference of the majority of the one or more users,the common geographic location of the majority of the one or more users,and the common need of the majority of the one or more users.

In another embodiment, in order to search the tailored content in thedatabase or the data warehouse, the computing system may query thedatabase or the data warehouse with the found keyword, e.g., by usingSQL.

Returning to FIG. 1, at 150, the computing system delivers the tailoredcontent, e.g., via the social network, etc., to the social networkgroup. In order to deliver the tailored content, the computing systemtransmits, via the social network, the tailored content to the socialnetwork group during a real-time chat session of one or more users inthe social network group and/or a real-time online interaction of theone or more users in the social network group.

In one embodiment, the computing system aggregates profile data, socialnetwork interaction data and social network postings (and metadata ofthe profile data, the interaction data and the postings), of a socialnetwork group or a subset of the group, e.g., by running a method shownin FIG. 2. The computing system normalizes those aggregated data to acommon set of data points, e.g., by classifying the aggregated data andidentifying a commonality represented by each class. The computingsystem further uses the common set of data points, e.g., the identifiedcommonality, etc., to determine content and a type of that content to beprovided to a corresponding social network group during a current onlinediscussion or an active data stream shared by the corresponding socialnetwork group. For example, the common set of data points may indicatethat the majority of users in the corresponding social network groupprefer a particular content type, e.g., MP3 file, etc. The computingsystem transforms the tailored content to fit to the preferred contenttype, e.g., by verbally recording a written news article. The computingsystem delivers the tailored content in the preferred content type tothe corresponding social network group, e.g., by sending a group emailto the corresponding social network group with an attachment of thetailored content in the preferred content type.

The computing system provides content, associated with the commonalitiesfound during the aggregation and the analysis, in the preferred type toa corresponding social network group. For example, if the correspondingsocial network group prefers to read a literature to obtain moreinformation on what is being discussed in an online social network groupdiscussion, the computing system may find one or more literature(instead of videos), e.g., by running an Internet search engine with akeyword of the online social network group discussion. The computingsystem may determine that keyword, e.g., by running content analysissoftware on texts exchanged during the online social network groupdiscussion. If the common set of data points indicates that the majorityof the users in the corresponding social network group prefers contentto be delivered in a video, the computing system may find content invideo/audio format which is associated with the online social networkgroup discussion, e.g., by querying one or more database or datawarehouse with the keyword via SQL. The aggregation and the analysis(i.e., methods shown in FIGS. 2 and 4) identify the commonalities acrossa social network group. The computing system finds the tailored contentand the preferred type based on the commonalities, e.g., by running anInternet search engine with a keyword representing one or morecommonalities. The tailored content (in the preferred type), which isdelivered to the group during the online social network groupdiscussion, is appropriate to the entire group. All participants in thegroup may be viewing the same content at the same time during the onlinesocial network group discussion.

In one embodiment, the computing system further receives, e.g., from adatabase associated with a social network, data encoding a day and timewhen each social network posting is created by one or more users in thatsocial network group. The computing system further receives ad-hoc(e.g., created on-the-fly without planning, etc.) subgroup data, e.g.,from a database associated with the social network. The receivedsubgroup includes, but is not limited to: data associated with eachmember in the subgroup in the social network group. When aggregating thereceived inputs, e.g., by running the method shown in FIG. 2, thecomputing system may aggregate these further received data with theinputs received at step 110 in FIG. 1. The computing system uses thisaggregation (and the analysis shown in FIG. 4) to determine a deliverymethod (e.g., an email, etc.) for the tailored content, to advertise aproduct to the social network group and/or to encourage contentconsumption (e.g., receiving the tailored content in) to the group,e.g., by identifying the commonalities across the group.

The following describes an example scenario that uses methods shown inFIGS. 1-2 and 4 in order to suggest content to a social network group.Joe, Tom, Cathy, and Ruth begin an online group chat in a socialnetwork. Currently, it is 11:00 a.m., and they are discussing thepossibility of going to lunch together. During the online group chat,they express interests in Chinese food, barbecue, and Indian food. Whilethey are chatting in online, the computing system aggregates the groupchat content, profiles of them, and history data of other interactionsamong them. By running methods shown in FIGS. 2 and 4 and/or a textmining technique, the computing system identifies that they are locatedin Raleigh, N.C. and that, previously, Joe, Tom, and Ruth enjoyed a mealat “Tandoor” restaurant (i.e., an Indian-Chinese buffet restaurant inRaleigh, N.C.). In this case, the computing system suggests theIndian-Chinese buffet restaurant, because the computing systemdetermines that the majority of them enjoyed Tandoor restaurant. Basedon commonalities identified by the aggregation and the analysis of thegroup chat content, the profiles and the history data, the computingsystem further determines that an audio-visual graphic is preferred by amajority of them, rather than a text, audio-only, or graphic-onlyrepresentation. The computing system presents the restaurant suggestion(i.e., the Indian-Chinese buffet restaurant) in an audio-visual format,viewable by all the four users of the group chat.

In one embodiment, a computing system may run the method illustrated inFIGS. 1-2 and 4. FIG. 3 illustrates examples of the computing system.Examples of the computing system may include, but are not limited to: aparallel computing system 300 including at least one processor 355 andat least one memory device 370, a mainframe computer 305 including atleast one processor 356 and at least one memory device 371, a desktopcomputer 310 including at least one processor 357 and at least onememory device 372, a workstation 315 including at least one processor358 and at least one memory device 373, a tablet computer 320 includingat least one processor 356 and at least one memory device 374, a netbookcomputer 325 including at least one processor 560 and at least onememory device 575, a smartphone 530 including at least one processor 361and at least one memory device 376, a laptop computer 335 including atleast one processor 362 and at least one memory device 377, a physicalserver 340 including at least one processor 361 and at least one memorydevice 378, a cloud computing system 397 including at least one storagedevice 398 and at least one server device 399, a software server 380,e.g., web server, HTTP server, application server, or a wearablecomputer 385, e.g., smartwatch, etc., including at least one processor390 and at least one memory device 395.

In one embodiment, the methods shown in FIGS. 1-2 and 4 may beimplemented as hardware on a reconfigurable hardware, e.g., FPGA (FieldProgrammable Gate Array) or CPLD (Complex Programmable Logic Device), byusing a hardware description language (Verilog, VHDL, Handel-C, orSystem C). In another embodiment, the method shown in FIGS. 1-2 and 4may be implemented on a semiconductor chip, e.g., ASIC(Application-Specific Integrated Circuit), by using a semi custom designmethodology, i.e., designing a semiconductor chip using standard cellsand a hardware description language.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the invention has been particularly shown and described withrespect to illustrative and preformed embodiments thereof, it will beunderstood by those skilled in the art that the foregoing and otherchanges in form and details may be made therein without departing fromthe spirit and scope of the invention which should be limited only bythe scope of the appended claims.

What is claimed is:
 1. A system for suggesting content to a group, the system comprising: a memory device; a processor connected to the memory device, wherein the processor is configured to perform: identifying a group of users in a social network participating in an active data stream; receiving inputs associated with the social network group, the inputs comprising postings by the users in the active data stream; aggregating the received inputs; analyzing the aggregated inputs as a whole and determining keywords that represent common interest of the users, the analyzing further determining a preferred content type of the users, the preferred content type of the users determined based on common set of data points in the aggregated inputs aggregated at least based on the postings by the users in the active data stream, the preferred content type of the users comprising at least one of an audio form, a video form, and literature form; searching, based on the aggregation and the analysis, for tailored content associated with the keywords, the tailored content to be delivered to the group in Internet, database and data warehouse; converting the tailored content to the preferred content type; and delivering the tailored content to the group in the social network.
 2. The system according to claim 1, wherein the received inputs further comprise at least one input selected from a group consisting of: social network postings of one or more of the users in the group, which are stored in a server device hosting the social network; previous or real-time chat sessions of the one or more of the users in the group, which are stored in the server device hosting the social network; photographs of the one or more users in the group, which are stored in the server device hosting the social network; previous or real-time videos of the one or more users in the group, which are stored in the server device hosting the social network; profile data of the one or more of the users in the group, which are stored in the server device hosting the social network; and previous or real-time interaction data of the one or more of the users in the group, which are stored in the server device hosting the social network.
 3. The system according to claim 1, wherein in order to perform the analyzing, the processor performs: classifying the aggregated inputs; identifying, based on the classification, a common interest, a common preference and a common need of the users; and further identifying, based on the classification, a common interest, a common preference and a common need of a majority of the users.
 4. The system according to claim 1, wherein the processor further performs: identifying, based on the aggregation and the analysis, a content type which is preferred by the group and a majority of the group.
 5. The system according to claim 4, wherein the processor further performs: transforming the tailored content to fit to the identified content type; and delivering the tailored content in the identified content type.
 6. The system according to claim 1, wherein the processor further performs: dynamically changing the one or more users in the group; and dynamically updating, in real-time, the received input in order to reflect the dynamically changed users.
 7. A computer program product for suggesting content to a group, the computer program product comprising a computer readable storage medium, the computer readable storage medium excluding a propagating signal, the computer readable storage medium readable by a processing circuit and storing instructions run by the processing circuit for performing a method, the method comprising: identifying a group of users in a social network participating in an active data stream; receiving inputs associated with the social network group, the inputs comprising postings by the users in the active data stream; aggregating the received inputs; analyzing the aggregated inputs as a whole and determining keywords that represent common interest of the users, the analyzing further determining a preferred content type of the users, the preferred content type of the users determined based on common set of data points in the aggregated inputs aggregated at least based on the postings by the users in the active data stream, the preferred content type of the users comprising at least one of an audio form, a video form, and literature form; searching, based on the aggregation and the analysis, for tailored content associated with the keywords, the tailored content to be delivered to the group in Internet, database and data warehouse; converting the tailored content to the preferred content type; and delivering the tailored content to the group in the social network.
 8. The computer program product according to claim 7, wherein the received inputs further comprise at least one input selected from a group consisting of: social network postings of one or more of the users in the group, which are stored in a server device hosting the social network; previous or real-time chat sessions of the one or more of the users in the group, which are stored in the server device hosting the social network; photographs of the one or more users in the group, which are stored in the server device hosting the social network; previous or real-time videos of the one or more users in the group, which are stored in the server device hosting the social network; profile data of the one or more of the users in the group, which are stored in the server device hosting the social network; and previous or real-time interaction data of the one or more of the users in the group, which are stored in the server device hosting the social network.
 9. The computer program product according to claim 7, wherein the analyzing comprises: classifying the aggregated inputs; identifying, based on the classification, a common interest, a common preference and a common need of the one or more users; and further identifying, based on the classification, a common interest, a common preference and a common need of a majority of the one or more users.
 10. The computer program product of claim 7, wherein the tailored content comprises content selected from the group consisting of: e-book, online magazine, online video, and online news articles. 